Method of block matching-based motion estimation in video coding

ABSTRACT

Motion estimation is efficient to reduce redundant information among successive frames in video compression applications. The blocks in the current frame can be replaced with the neighboring blocks in the spatial directions in the previous frame with small errors. Many types of motion estimation methods such as Block Matching Algorithm are widely used to take a balance between a good image quality and the computation complexity. A Block Matching Algorithm named as New Cellular Search (NCS) Algorithm utilizes two particular search patterns: HCSP and VCSP, in the horizontal and vertical directions to search the best motion vector. Three performance measurements including peak signal to noise ration (PSNR), Average Search Point (ASP), and Mean Square Error (MSE) are used to compare this new search algorithm with some major motion estimations like FS, TSS, CS, and NCDS. The NCS is very efficient in computation reduction while keeping the almost same picture quality.

CROSS-REFERENCED TO RELATED APPLICATIONS

This application is a continuation-in-part of application Ser. No.11/389,718 filed Mar. 27, 2006 entitled “Method for Block Matching-BasedMotion Estimation in Video Coding” and which is hereby incorporatedherein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a method for block matching-based motionestimation in video coding and particularly to a novelblock-matching-based motion estimation, in the field of video coding, toreduce inter-frame redundancy.

2. Description of Related Art

Motion Estimation is an effective method of reducing inter-frameredundancy at the time of video coding. Generally speaking, in themotion estimation technology, comparison between previous videos is madeto search a piece of adequate video information as a substitute, whichis thus an operation process that is continuously repeated and requiresa great deal of data for comparison. In different methods of motionestimation, block matching is a very simple and extremely effectivemethod. Conventionally, there are many motion estimation algorithms thathave been proposed to reduce the high complexity of calculation of theblock matching and to meanwhile keep a good image quality. Among them,the cellular search algorithm is currently generally accepted to beeffective and speedy.

Consequently, because of the technical defects described above, theapplicant has kept on carving unflaggingly through wholeheartedexperience and research to develop the present invention, which caneffectively improve the defects described above.

SUMMARY OF THE INVENTION

In a technical problem to be solved with this invention, owing to acurrent technology of cellular algorithm, there are still excessivesearch points so that image blocks are compared still too slow, which isnecessarily improved.

To solve the problem, a novel block-matching-based motion estimation invideo coding according to this invention is provided, which can berapidly processed using a computing device well-known to those skilledin the art. The present invention clearly simplifies and reduces theamount of data that needs to be processed to arrive at the presentmotion estimation and includes three steps.

Step 1, the origin in the area of search is first set to a centralsearch point in the Horizontal Cellular Search Pattern (HCSP) and thecoordinates are set to (0, 0). Next, error values between six candidatepoints and the central point around the block and HCSP are calculated.If a minimum error value occurs at the central point, the process jumpsto step 3; if it occurs at the rest of six candidate points outside,then step 2 is executed.

Step 2, if the minimum error value occurs horizontally, the position ofthe minimum error value as Mean Absolute Distance (MAD) searched at step1 is set to a new central point in HCSP; on the contrary, if the minimumerror value occurs vertically, it is set to a new central point inVertical Cellular Search Pattern (VCSP) and re-calculation is made for anew error value as MAD. If the minimum error value as MAD lies in thecentral point in the mode of HCSP or VCSP, a jump is made to step 3otherwise step 2 is repeated.

Step 3, MAD values at points 3 and 6 are compared. If the MAD value atpoint 3 is lower, MAD values at points 1 and 8 are calculated and twolower MAD values are found; If the MAD value at point 6 is lower, MADvalues at points 1 and 9 are calculated and two minimum MAD values arefound. Finding the coordinates of minimum error value as MAD at thisstep is exactly finding an optimal motion vector for the matching block.

Advantageously, the candidate block and the current block at step 2 arecompared in HCSP and VCSP with the minimum error values given in themeasurement algorithm.

At step 2, 7 candidate points are not required for calculation at eachtime of search, only 3 candidate points may be required for calculation.

From the effects of previous technologies, based on peak signal to noiseratio (PSNR), Average Search Point (ASP), and Mean Square Error (MSE) asobjective methods of performance and efficiency measurement, an imagemotion estimation algorithm and a full search (FS) algorithm, athree-step search (TSS) algorithm, a cellular search (CS) algorithm, anda new cellular diamondoid search (NCDS) algorithm that have beenproposed are compared with each other for performance. Apparent from anexperiment, the accuracy in the proposed method of measurement is less50% than that in the conventional cellular algorithm, and the quality ofa picture is still in a certain level. In other words, in the novelblock-matching-based motion estimation, an image block may be comparedmore quickly, a technology of video signal compression and decompressionbeing effectively improved very much.

However, in the description mentioned above, only the preferredembodiments according to this invention are provided without limit tothis invention and the characteristics of this invention; all thoseskilled in the art without exception should include the equivalentchanges and modifications as falling within the true scope and spirit ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view illustrating an HCSP search pattern and aVCSP search pattern according to this invention;

FIG. 2 is a schematic view illustrating search points according to thisinvention, in which points 1 through 7 are the search points at step 1and points 8 and 9 are the search points at step 3;

FIG. 3 is a schematic view illustrating search motion patterns of HCSPand VCSP according to this invention; and

FIG. 4 is a flow chart of block matching-based motion estimation inaccordance with the invention.

FIG. 5 depicts the calculation of steps 1-3 using a computer and outputdevice for processing steps 1-3.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Now, the present invention will be described more specifically withreference to the following embodiments. It is to be noted that thefollowing descriptions of preferred embodiments of this invention arepresented herein for purpose of illustration and description only; theyare not intended to be exhaustive or to be limited to the precise formdisclosed.

A New Cellular Search (NCS) Algorithm provided in this invention is likea general block search algorithm assuming that, in a search window, themore a candidate block and a current block lie far from an optimalmatching block, the more an error value becomes, and the less thecandidate block and the current block lie far from the optimal matchingblock, the less the error value becomes. Under such a common view, thealgorithm is described below. The estimation may be carried out using aprocessing device well-known in those skilled in the art.

In the novel block-matching-based motion estimation, two search modesare used, respectively a Horizontal Cellular Search Pattern (HCSP) and aVertical Cellular Search Pattern (VCSP), as shown in FIG. 1. In theprocess of search, when a minimum value of block distortion is not inthe search pattern of central point, HCSP and VCSP are used alternately.If the minimum value of block distortion occurs horizontally, HCSP isused, or if the minimum value of block distortion occurs vertically VCSPis used. The steps of novel block-matching-based motion estimation aredescribed below in conjunction with FIG. 2.

At step 1, 7 points in HCSP are calculated.

The origin in the area of search is first set to a central search pointin the pattern of HCSP and the coordinates are set to (0, 0). Next,error values between six candidate points and the central point aroundthe block and HCSP are calculated. If a minimum error value occurs atthe central point, jump to step 3; if it occurs at the rest of sixcandidate points outside, then go on to execute step 2.

At step 2, newly added search points are re-calculated in HCSP or VCSP.

If the minimum error value occurs horizontally, the position of theminimum error value as MAD searched at step 1 is set to a new centralpoint in HCSP; on the contrary, if the minimum error value occursvertically, it is set to a new central point in VCSP. Calculation ismade again for a new error value as MAD. If the minimum error value asMAD lies in the central point of HCSP or VCSP, directly jump to step 3or else repeat step 2.

At step 3, comparison is made between internal points 1 and 8 orinternal points 1 and 9 to get a minimum MAD value. MAD values at points3 and 6 are compared. If the MAD value at point 3 is lower, MAD valuesat points 1 and 8 are calculated and two lower MAD values are found; Ifthe MAD value at point 6 is lower, MAD values at points 1 and 9 arecalculated and two minimum MAD values are found. Finding the coordinatesof minimum error value as MAD at this step is exactly finding an optimalmotion vector for the matching block in the novel block-matching-basedmotion estimation.

HCSP and VCSP are symmetrical search patterns, so at step 2, all 7candidate points are not required for calculation at each time ofsearch, as only 3 candidate points are required for calculation. Therequired internal search points are less than those in cellular searchalgorithm. The search motion pattern is shown in FIG. 3.

FIG. 4 is a flow chart of novel block-matching-based motion estimationshowing the three steps of calculating the seven points of HCSP,newly-added search points are re-calculated in HCSP or VCSP, and acomparison is made between internal points 1 and 8 or internal points 1and 9 to get a minimum MAD value, thus an optimal motion vector for thematching block is found.

FIG. 5 depicts the calculations of Steps 1-3 using a computer 10 andoutput device 12.

For a valuation of performance according to this invention in the motionestimation algorithm, search speed (number of candidate points to besearched) and frame quality are required. In a general algorithm, thequality of image frame is mostly evaluated with peak signal to noiseratio (PSNR), as equation (1).

$\begin{matrix}{{P\; S\; N\; R} = {10\log_{10}\frac{255 \times 255}{\frac{1}{N \times N}{\sum\limits_{x = 1}^{N}{\sum\limits_{y = 1}^{N}{{{f\left( {x,y} \right)} - {f^{\prime}\left( {x,y} \right)}}}^{2}}}}}} & (1)\end{matrix}$

In the equation, f(x, y) is an original frame.

In an experiment, six standard films, Salesman, Football, Susigirl,Claire, Hall, and Miss are used for analysis, and 50 pictures are used,each of which the size is 352×288 pixels, the gamma is 8 bits, and theblock size is 16×16 pixels respectively. At this time, the minimum errorvalue as MAD is used to determine whether a minimum distorted block isfound. Further, the full search (FS) algorithm, the three-step search(TSS) algorithm, the cellular search (CS) algorithm, and the newcellular diamondoid search (NCDS) algorithm are compared with each otherfor performance.

Apparent from table 1 shown below, the PSNR given from the NCS algorithmis about the same as that given from the CS algorithm.

TABLE 1 Mean Performance evaluation (PSNR) Algorithm Film FS TSS CS NCDSNCS Salesman 35.2744 35.1092 35.0758 34.4639 34.8216 Susigirl 34.705234.5401 34.4879 33.6261 33.9535 Football 22.9678 22.3814 22.3336 21.959622.1160 Claire 41.9041 41.8603 41.6439 40.9031 41.4042 Hall 34.463934.4026 34.3386 34.2148 34.2947 Miss 39.0471 38.9535 38.8865 37.982638.4725

Also apparent from table 2, the search points in the NCS algorithm areeven less half than those in the CS algorithm.

TABLE 2 Average Search Point (ASP) in Each Block Algorithm Film FS TSSCS NCDS NCS Salesman 225 25 15.8312 10.5277 7.7170 Susigirl 225 2517.9404 12.1550 9.8262 Football 225 25 22.3453 12.7196 11.0504 Claire225 25 16.1315 10.1931 7.2645 Hall 225 25 17.0067 10.7624 8.5537 Miss225 25 20.1082 12.9847 11.0690

Also apparent from table 3, MSE given in the NCS algorithm is lessdifferent from that given in the CS algorithm.

TABLE 3 Indication of MSE Algorithm Film FS TSS CS NCDS NCS Salesman19.2649 19.5295 20.6966 23.7482 22.0547 Susigirl 23.5344 25.1683 26.716831.8301 30.0965 Football 331.0834 374.5183 383.5360 417.8716 402.8845Claire 4.1935 4.3136 4.7191 5.8144 5.8814 Hall 23.5380 25.2739 26.861328.5278 28.4947 Miss 8.0442 8.3668 8.5281 10.4580 9.0528

Complete results given from comparison and analysis are separatelylisted in tables 1 through 3. The evaluations of PSNR, Average SearchPoint (ASP), and MSE are included in the results.

In the item of PSNR, the NCS algorithm for the films of Salesman,Susigirl, Claire, Miss, and Hall is very similar to the CS algorithm,indicating that the five types of pictures are close in quality.

Compared with the MSE value in the NCDS algorithm for all the films, theMSE value in the NCS algorithm is minimum. Compared with the values inthe CS algorithm for the films of Salesman, Susigirl, Claire, Miss, andHall, the values in the NCS algorithm are less different; namely, thetwo algorithms are similar to each other in block matching, the valuesin the two algorithm being acceptable.

In the aspect of mean search point, regardless of the fast or slowmotion of contents, for example, in the films of Salesman, Susigirl,Claire, Miss, and Hall, the search points in the NCS algorithm arealmost less half than those in the CS algorithm. Namely, the quantity ofcalculation in the NCS algorithm is less than that in the CS algorithm.

To sum up, it is apparent that the picture quality given in the novelblock-matching-based motion estimation is almost equal to that given inthe cellular search algorithm, but much complexity is reduced at thetime of operation.

Generally speaking, a complete procedure of video compression shouldcomprise the following steps.

-   -   1. Inputted data is moved to a RAM or a buffer.    -   2. DC compositions are removed.    -   3. The best motion estimation is found to compute computing the        motion compensation and prediction errors.    -   4. In consideration of current bandwidth and storage space, an        optimal encoding codec is found.

In this invention, the best motion estimation is emphasized and consumesmaximum runtime in the whole process of video compression. Generally, inthe environment of a PC working, the number of mean search points ineach block is direct proportional to the runtime, so the performance ofruntime may be compared depending on the number of mean search points ineach block.

Further, the amount of runtime also has something to do with thecontents of input video data. As described above, the input datacomprises a film moving fast, such as the film of Football, and a filmmoving slowly, such as the film of Claire. When the NCS algorithm isused, the runtime may be lowered to 45% through 54% respectively for thetwo types of films. When it is compared with the NCDS algorithm, it isapparent that the runtime may be lowered to 10% through 14% for the filmmoving fast and to 19% through 28% for the film moving slowly. In thecase of image quality, when the CS algorithm is compared with the NCDSalgorithm, the image quality of 0.2 db through 0.4 db gets lostrespectively, and the quantity in the form of a digit is very hard tofind with naked eyes. Namely, the image quality given from the NCSalgorithm is similar to that given from the algorithms of CS and NCDS,but the quality is lowered very much at the time of operation.

To sum up, compared with the conventional block estimation technology(the cellular algorithm), the algorithm according to this inventionreduces 50% calculation process, and in the algorithm, the image blocksmay even be compared more quickly and the technology of video signalcompression and decompression is effectively improved very much; itcompletely meets the requirements of application for the patent, andhence we apply following the patent law; we earnestly request you toexamine it for details and to approve the patent as soon as possible forprotection of the inventor's rights and interests.

While the invention has been described in terms of what is presentlyconsidered to be the most practical and preferred embodiments, it is tobe understood that the invention needs not be limited to the disclosedembodiment. On the contrary, it is intended to cover variousmodifications and similar arrangements included within the spirit andscope of the appended claims which are to be accorded with the broadestinterpretation so as to encompass all such modifications and similarstructures.

What is claimed is:
 1. A method of block-matching-based motionestimation in video coding by finding an optimal motion vector for thematching block using a data processing device, comprising the followingsteps, in which: step 1, an origin in a search area is first set to acentral search point in a Horizontal Cellular Search Pattern (HCSP) andcoordinates are set to (0, 0); next, error values between six candidatepoints and the central point around a block and HCSP are calculated; ifa minimum error value occurs at the central point, jump to step 3, andif it occurs at the rest of six candidate points outside, then go on toexecute step 2; step 2, if the minimum error value occurs horizontally,a position of the minimum error value as Mean Absolute Distance (MAD)searched at step 1 is set to a new central point in HCSP; on thecontrary, if the minimum error value occurs vertically, it is set to anew central point in Vertical Cellular Search Pattern (VCSP) andre-calculation is made for a new error value as MAD; if the minimumerror value as MAD lies in the central point of HCSP or VCSP, directlyexecute step 3, if not repeat step 2; and step 3, MAD values at points 3and 6 are compared; if the MAD value at point 3 is lower, a point 8between points 1 and 3 is added as a candidate point and MAD values atpoints 1 and 8 are calculated and two lower MAD values are found, and ifthe MAD value at point 6 is lower, a point 9 between points 1 and 6 isadded as a candidate point and MAD values at points 1 and 9 arecalculated and two minimum MAD values are found; finding the coordinatesof minimum error value as MAD at this step finds an exact optimal motionvector for the matching block.
 2. A method for block-matching-basedmotion estimation in video coding according to claim 1, wherein thecandidate block and the current block at step 2 are compared in HCSP andVCSP with the minimum error values given in the measurement algorithm.3. A method for block-matching-based motion estimation in video codingaccording to claim 1, wherein at step 2, at each time of search, only 3candidate points may be required for calculation.